The article discusses the increasing complexity of Kubernetes and suggests that Silicon Valley is exploring alternative technologies for container orchestration, citing a benchmark showing a stripped-down stack outperforming Kubernetes.
A list of 10 Python libraries that can improve your workflow and make coding easier.
1. **boltons:** Collection of useful Python utilities, filling gaps in the standard library.
2. **tenacity:** Elegant retrying mechanism for functions that might fail.
3. **diskcache:** Persistent caching system backed by a disk (SQLite).
4. **glom:** Toolkit for easily accessing nested data structures (like JSON).
5. **tqdm.contrib.concurrent:** Multi-threaded progress bars using `tqdm`.
6. **anyio:** Compatibility layer for asynchronous Python code across different libraries.
7. **deepdiff:** Detects differences between Python objects (dicts, lists, etc.).
8. **pyrsistent:** Immutable data structures for functional programming.
9. **structlog:** Library for creating structured, parseable logs.
10. **pyinstrument:** Python profiler that generates flamegraphs for performance analysis.
1. **boltons:** Collection of useful Python utilities, filling gaps in the standard library.
2. **tenacity:** Elegant retrying mechanism for functions that might fail.
3. **diskcache:** Persistent caching system backed by a disk (SQLite).
4. **glom:** Toolkit for easily accessing nested data structures (like JSON).
5. **tqdm.contrib.concurrent:** Multi-threaded progress bars using `tqdm`.
6. **anyio:** Compatibility layer for asynchronous Python code across different libraries.
7. **deepdiff:** Detects differences between Python objects (dicts, lists, etc.).
8. **pyrsistent:** Immutable data structures for functional programming.
9. **structlog:** Library for creating structured, parseable logs.
10. **pyinstrument:** Python profiler that generates flamegraphs for performance analysis.
A deep dive into advanced evaluation for data scientists, discussing why accuracy is often misleading and exploring alternative metrics for classification and regression tasks like ROC-AUC, Log Loss, R², RMSLE, and Quantile Loss.
The article discusses using Large Language Model (LLM) embeddings as features in traditional machine learning models built with scikit-learn. It covers the process of generating embeddings from text data using models like Sentence Transformers, and how these embeddings can be combined with existing features to improve model performance. It details practical steps including loading data, creating embeddings, and integrating them into a scikit-learn pipeline for tasks like classification.
An analysis of Claude's extensive system prompt, highlighting its components, including tool definitions, behavior instructions, and how it reflects Anthropic's development priorities. The article details changes between Claude 3.7 and 4.0, revealing a shift towards encouraging search functionality and addressing user-observed issues.
timep is an efficient and accurate state-of-the-art trap-based profiler and flamegraph generator for bash code. It maps the full call-stack tree for the bash code being profiled, and (optionally) uses that call-stack tree to generate a FlameGraph of the profiled bash commands!
A pair of landmark studies has identified the originators of the Indo-European family of languages in current-day Russia about 6,500 years ago, the Caucasus Lower Volga people.
>“We can see there was a small group of villages 5,700 to 5,300 years ago with just a couple thousand breeding individuals,” Reich said. “And then there was a demographic explosion, with these people going everywhere.”
>“We can see there was a small group of villages 5,700 to 5,300 years ago with just a couple thousand breeding individuals,” Reich said. “And then there was a demographic explosion, with these people going everywhere.”
New research identifies the prehistoric progenitors of Uralic languages in northeastern Siberia, about 4,500 years ago, challenging previous theories that placed their origins near the Ural Mountains.
The official Python SDK for Model Context Protocol servers and clients. It allows building MCP clients, servers, and provides tools for interacting with LLMs in a standardized way.
Grep now supports the Model Context Protocol (MCP), enabling AI apps to query a million public GitHub repositories using a standard interface. This allows AI agents to search code and retrieve relevant snippets for tasks like error handling and implementation guidance.
